SM-GMVAE: An intelligent model for defect quantification evaluation based on few ultrasonic signals

Abstract

Abstract The conventional defect quantification evaluation approaches based on machine learning requires massive amounts of labeled defect signals, which is expensive and time-consuming works. This paper proposed a novel Similarity Metric Gaussian Mixture Variational Auto-Encoder (SM-GMVAE) model, which enables quantify defect with few labeled defect signals. The SM-GMVAE model is designed based on few-shot learning, which includes two modules: feature extraction (FE) module and similarity metric (SM) module. The FE module is designed to extract the feature of defect signal via the Variational Auto-Encoder (VAE). The SM module is used to measure the similarity of two defect signals based on the Gaussian Mixture Model (GMM). Moreover, sparse filtering techniques are used to enhance the sparsity of the features in the SM module. To validate proposed model, some specimens with four various depth defects are designed and fabricated for ultrasonic nondestructive testing experiments. A dataset with defects of different depths is established to compare proposed model with other methods. Our method obtains state-of-the-art experimental results with few labeled defect signals. Different from many published papers, our model is trained with few labeled data, which is more close to engineering practical application than other evaluation model trained using large numbers of labeled data. In other words, the developed approach can realize more complex defect evaluation tasks (such as: size, location, shapes, etc.) at very low data labeling cost.